Bibliographic record
Abstract
The purpose of the following study was to develop a comprehensive, theory-based framework for creating instructional video podcasts designed to present worked examples. Sixteen design characteristics, organized according to four categories (establishing context, providing effective explanations, minimizing cognitive load, and engaging students), were used to develop 59 pre-calculus videos for 856 first-year university students. Overall, the vast majority of students noted that the video podcasts were useful and helped them understand mathematics better. With respect to establishing context, the evidence suggested that problem selection was appropriate and video podcasts were clear, straightforward, and detailed. Regarding the quality of explanations, a number of students commented on the effectiveness of the step-by-step presentation of solutions and the use of visuals to support learning. Students agreed that video podcasts were easy to read, but did not directly mention other issues involving cognitive load. Students also noted that video podcasts were engaging and better than using textbooks. They also enjoyed working on the interactive student-problems. Finally, significant gains were observed in all five pre-calculus knowledge categories evaluated. It is concluded that the framework proposed in this study is a reasonable starting point for creating effective worked-example video podcasts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".